Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	134,654
2	South Dakota	132,530
3	Rhode Island	128,166
4	Utah	119,970
5	Tennessee	116,091
6	Arizona	115,389
7	Iowa	110,841
8	Oklahoma	110,430
9	Arkansas	109,327
10	Wisconsin	108,943
11	Nebraska	107,974
12	South Carolina	106,667
13	Alabama	104,909
14	Kansas	104,248
15	Mississippi	102,352
16	Indiana	102,091
17	New Jersey	100,949
18	Idaho	100,450
19	Nevada	98,401
20	Illinois	97,948
21	Montana	97,479
22	Georgia	97,095
23	Wyoming	96,838
24	Delaware	96,596
25	Texas	95,977
26	Kentucky	95,893
27	Louisiana	95,308
28	Florida	95,168
29	Missouri	95,077
30	New York	95,056
31	California	92,662
32	Minnesota	91,342
33	Massachusetts	91,335
34	New Mexico	91,112
35	North Carolina	86,866
36	Ohio	86,544
37	Connecticut	85,606
38	Alaska	84,402
39	Colorado	80,022
40	Pennsylvania	79,418
41	West Virginia	78,460
42	Michigan	72,427
43	Virginia	72,094
44	Maryland	67,524
45	District of Columbia	62,593
46	New Hampshire	61,062
47	Washington	47,756
48	Puerto Rico	43,868
49	Oregon	38,872
50	Maine	37,084
51	Vermont	30,160
52	Hawaii	20,854

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	New Jersey	554
2	New York	438
3	Michigan	371
4	Massachusetts	353
5	Delaware	350
6	Vermont	323
7	Pennsylvania	291
8	Minnesota	275
9	Florida	257
10	South Carolina	255
11	West Virginia	253
12	New Hampshire	242
13	District of Columbia	238
14	South Dakota	233
15	Maryland	223
16	Colorado	220
17	Illinois	204
18	Virginia	199
19	North Dakota	168
20	Maine	163
21	Nebraska	160
22	Connecticut	158
23	Ohio	155
24	Rhode Island	152
25	Indiana	149
26	Utah	141
27	Georgia	133
28	Puerto Rico	131
29	Iowa	129
30	Texas	126
31	Wisconsin	113
32	Kentucky	105
33	Missouri	104
34	Washington	103
35	Hawaii	96
36	Nevada	95
37	Idaho	93
38	New Mexico	88
39	Arizona	87
40	Alabama	85
41	Alaska	84
42	Oregon	84
43	Mississippi	75
44	Wyoming	72
45	Montana	69
46	North Carolina	69
47	California	67
48	Tennessee	67
49	Kansas	50
50	Arkansas	46
51	Oklahoma	39
52	Louisiana	32

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,745
2	New York	2,546
3	Massachusetts	2,483
4	Rhode Island	2,461
5	Mississippi	2,352
6	Arizona	2,324
7	Connecticut	2,205
8	South Dakota	2,185
9	Louisiana	2,169
10	Alabama	2,146
11	North Dakota	1,965
12	Pennsylvania	1,957
13	Indiana	1,934
14	New Mexico	1,872
15	Illinois	1,856
16	Arkansas	1,853
17	Iowa	1,814
18	South Carolina	1,771
19	Georgia	1,728
20	Tennessee	1,715
21	Michigan	1,706
22	Nevada	1,699
23	Kansas	1,678
24	Texas	1,664
25	Delaware	1,585
26	Ohio	1,584
27	Florida	1,544
28	District of Columbia	1,496
29	California	1,490
30	West Virginia	1,469
31	Missouri	1,457
32	Maryland	1,364
33	Kentucky	1,356
34	Montana	1,343
35	Wisconsin	1,249
36	Oklahoma	1,225
37	Minnesota	1,224
38	Wyoming	1,200
39	Virginia	1,194
40	Nebraska	1,179
41	North Carolina	1,149
42	Idaho	1,095
43	Colorado	1,070
44	New Hampshire	909
45	Washington	694
46	Puerto Rico	661
47	Utah	659
48	Oregon	565
49	Maine	547
50	Alaska	408
51	Vermont	360
52	Hawaii	324

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Georgia	5
2	Massachusetts	5
3	California	4
4	Kentucky	4
5	Ohio	4
6	Florida	3
7	New York	3
8	Texas	3
9	Arizona	2
10	Arkansas	2
11	Delaware	2
12	Illinois	2
13	Indiana	2
14	Missouri	2
15	Nevada	2
16	New Jersey	2
17	Pennsylvania	2
18	South Carolina	2
19	South Dakota	2
20	Utah	2
21	Alabama	1
22	District of Columbia	1
23	Iowa	1
24	Kansas	1
25	Louisiana	1
26	Maine	1
27	Maryland	1
28	Michigan	1
29	Mississippi	1
30	New Hampshire	1
31	New Mexico	1
32	Tennessee	1
33	Vermont	1
34	Virginia	1
35	West Virginia	1
36	Alaska	0
37	Colorado	0
38	Connecticut	0
39	Hawaii	0
40	Idaho	0
41	Minnesota	0
42	Montana	0
43	Nebraska	0
44	North Carolina	0
45	North Dakota	0
46	Oklahoma	0
47	Oregon	0
48	Puerto Rico	0
49	Rhode Island	0
50	Washington	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	353,077	1	99
Chattahoochee	Georgia	314,935	2	99
Bent	Colorado	265,914	3	99
Dewey	South Dakota	243,890	4	99
Lincoln	Arkansas	243,857	5	99
Davidson	Tennessee	135,105	208	93
Richland	South Carolina	105,078	1030	67
York	South Carolina	101,253	1208	61
Orange	California	83,694	2022	35
Pierce	Washington	46,344	2906	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Gove	Kansas	8,346	2	99
Jerauld	South Dakota	7,948	3	99
Galax city	Virginia	7,878	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,488	1765	43
Davidson	Tennessee	1,282	2015	35
York	South Carolina	1,274	2026	35
Richland	South Carolina	1,268	2035	35
Pierce	Washington	666	2729	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.